For a decade, numerous methods for few-shot segmentation have been proposed [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, and most methods follow a learning-to-learn paradigm [14]} to avoid the risk of overfitting due to insufficient training data. Since the prediction for query image should be conditioned on support images and corresponding masks, the key to this task is how to effectively utilize provided support samples. Although formulated in various ways, most early efforts [15]}, [16]}, [3]}, [2]} attempted to utilize a prototype extracted from support samples. However, such approaches disregard pixel-wise relationships between support and query features or spatial structure of features, which may lead to sub-optimal results.
